Exploring Paxlovid Efficacy in COVID-19 Patients with MAFLD: Insights from a Single-Center Prospective Cohort Study

This study investigates the intricate interplay between Metabolic-associated Fatty Liver Disease (MAFLD) and COVID-19, exploring the impact of MAFLD on disease severity, outcomes, and the efficacy of the antiviral agent Paxlovid (nirmatrelvir/ritonavir). MAFLD, affecting a quarter of the global population, emerges as a potential risk factor for severe COVID-19, yet the underlying pathophysiological mechanisms remain elusive. This study focuses on the clinical significance of Paxlovid, the first orally bioavailable antiviral agent granted Emergency Use Authorization in the United States. Notably, outcomes from phase II/III trials exhibit an 88% relative risk reduction in COVID-19-associated hospitalization or mortality among high-risk patients. Despite conflicting data on the association between MAFLD and COVID-19 severity, this research strives to bridge the gap by evaluating the effectiveness of Paxlovid in MAFLD patients with COVID-19, addressing the scarcity of relevant studies.


Introduction
In an endeavor to gain a more comprehensive understanding of COVID-19 and identify potential therapeutic interventions, the pandemic has given rise to scientific investigations that have unveiled novel insights into the intricate interplay between Metabolic-associated Fatty Liver Disease (MAFLD) and infection [1,2].
MAFLD, a prevalent cause of chronic liver disease, affects a quarter of the global population [3,4].It is recognized as a sensitive and pivotal indicator of metabolic dysfunction [3].
Several studies posit that MAFLD constitutes a noteworthy risk factor for the acquisition of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and subsequent hospitalization, independently of other components of the metabolic syndrome.Moreover, there is a potential association with heightened disease severity, prolonged hospitalization, and unfavorable outcomes [5,6].Nevertheless, the pathophysiological mechanisms through which MAFLD exacerbates COVID-19 remain undisclosed.One proposed hypothesis suggests that MAFLD exacerbates the phenomenon of the "cytokine storm" through the hepatic release of pro-inflammatory cytokines [7].Current research indicates that COVID-19 patients with coexisting MAFLD exhibit a distinct cytokine profile, characterized by elevated levels of interleukin (IL)-6, IL-8, IL-10, and C-X-C motif chemokine ligand 10 (CXCL10), all of which are implicated in a more severe clinical presentation [8][9][10].
Meta-analyses have postulated that the presence of MAFLD heightens the risk of severe progression of COVID-19 and augments the likelihood of patients requiring admission to intensive care units [1,6,[11][12][13].However, its impact on the development of critical COVID-19 or mortality remains equivocal [14].
Viruses 2024, 16, 112 3 of 24 All patients received standard treatment in accordance with the national treatment protocol for COVID-19.This regimen encompassed symptomatic antipyretic therapy (utilizing paracetamol or ibuprofen), mucolytic agent and expectorant (Ambroxol), anticoagulant therapy (administered through low-molecular-weight heparins, such as enoxaparin at a dosage of 40 mg or 4000 IU anti-Xa), antimicrobial treatment for co-infections (comprising amoxicillin/clavulanate in addition to macrolides such as azithromycin or clarithromycin, or cephalosporins of the II-III generation alongside macrolides), corticosteroids (administered intravenously at a dose of 0.15 mg/kg of dexamethasone once daily, with a dose of 8-16 mg, for a duration of 7-10 days), and non-invasive oxygen support.
The primary outcome was the length of hospital stay (number of days spent by participants in the hospital from the day of admission up to the day of their discharge).The secondary outcomes included the SpO 2 level after-before dynamics depending on Paxlovid treatment.
Finally, 33 patients with MAFLD and 39 without MAFLD were included in the study.Eleven patients from the MAFLD group and twelve patients from the non-MAFLD group were administered nirmatrelvir-ritonavir (Paxlovid) according to the Food and Drug Administration (FDA) recommendations [24].
The enlisted participants were not previously involved in any prior investigations, and each individual explicitly granted written informed consent.This study was approved by the I. Horbachevsky Ternopil National Medical University Ethics Committee (protocol No. 72).

Laboratory and Clinical Data
At the time of hospital admission, baseline patient characteristics, including comorbidities, baseline clinical status, and vital parameters were collected.

Statistical Analysis
The clinical characteristics, laboratory parameters, and demographic information underwent meticulous assessment, and their presentation was conducted through descriptive statistics, featuring frequencies and medians along with interquartile ranges.To compare the two independent groups, Fisher's exact test and the Mann-Whitney U test were employed.For comparisons involving three or more groups, the Kruskal-Wallis test with Dunn's multiple comparisons test was applied.In instances requiring a comparison between two related groups, the Wilcoxon signed-rank test was utilized.All statistical tests conducted were two-tailed, with statistical significance defined as a p-value less than 0.05.Spearman's correlation was used with two continuous variables, the point-biserial correlation between binary and continuous data, and the Chi-square test between two binary data, summarized in a correlation matrix.ROC analysis was used to assess the quality of a binary logistic regression model.Comparing time to hospital discharge between Paxlovid and standard therapy groups was evaluated using the Kaplan-Meier method and hazard ratios (HR) with 95% confidence intervals (95% CI) and p-values that were calculated via the log-rank test.Risk factors associated with COVID-19 severity, the need for oxygen supply, and factors to predict the Paxlovid therapy were investigated using a univariate and subsequently multivariable logistic regression analysis.The strength of association was expressed as an odds ratio (OR) and its corresponding 95% CI.Statistical analyses were performed using GraphPad Prism Software version 8.4.3 (San Diego, CA, USA), IBM SPSS Statistics 25, and Jamovi 2.4.11.
The resulting regression model is statistically significant (p < 0.001).Nagelkerke R 2 0.811 indicates a strong relationship between predictors and observed the need for oxygen supply.The model achieves a high predictive accuracy, with 91.5% of predictions correctly classified.
When evaluating the dependence of the probability of odds on the value of logistic function p using the ROC analysis, the following curve was obtained (Figure 1).
The cut-off value of the logistic function p which corresponds to the highest Youden's J statistic is 0.29.The specificity and sensitivity of the method were 92.2% and 90%, respectively (Figure 1).Paxlovid-treated patients had significantly lower lengths of hospital stay (9 days, IQR 7-11 days vs. 11 days, IQR 9-14 days, p = 0.001).The presence of MAFLD itself did not affect the duration of hospitalization (Figure 2).
When evaluating the dependence of the probability of odds on the value of logistic function p using the ROC analysis, the following curve was obtained (Figure 1).The area under the ROC curve comprised 0.96 with 95% CI: 0.91-1.00.The resulting model was statistically significant (p < 0.001).

Kaplan-Meier Test for Recovery Time
The Kaplan-Meier involves computing probabilities of the occurrence of an event at a certain point in time.We examined the impact of Paxlovid treatment on time to recovery, as defined by time to hospital discharge.In survival analysis using Kaplan-Meier estimates, the appointment of the Paxlovid (HR 1.85, 95% CI 1.04 to 3.30, p = 0.005) appeared to be an efficient prognostic marker associated with shorter time to recovery, as presented in Figure 14.

Kaplan-Meier Test for Recovery Time
The Kaplan-Meier involves computing probabilities of the occurrence of an event at a certain point in time.We examined the impact of Paxlovid treatment on time to recovery, as defined by time to hospital discharge.In survival analysis using Kaplan-Meier estimates, the appointment of the Paxlovid (HR 1.85, 95% CI 1.04 to 3.30, p = 0.005) appeared to be an efficient prognostic marker associated with shorter time to recovery, as presented in Figure 14.
We create a simple logistic regression for predicting Paxlovid therapy (Table 6).This predictive model has developed conditioning on SpO 2 (admission), length of hospital stay (days), monocytes (discharge), and fibrinogen (discharge).Hazard ratios (HR) with 95% confidence intervals and p-values were calculated using the log-rank test.We defined the probability of hospital discharge in a given length of time while considering time in many small intervals.The day of discharge from the hospital was considered the target event.p > 0.05 shows statistically significant difference between medians of hospital discharge (standard therapy-11 days vs. Paxlovid therapy-9 days).
We create a simple logistic regression for predicting Paxlovid therapy (Table 6).This predictive model has developed conditioning on SpO2 (admission), length of hospital stay (days), monocytes (discharge), and fibrinogen (discharge).
The resultant regression model exhibits statistical significance (p < 0.001).A Nagelkerke R 2 value of 0.321 suggests a robust relationship between predictors and Paxlovid treatment.This model achieves an accuracy of 68.1%, accurately classifying the predictions.
When evaluating the dependence of the probability of odds on the value of logistic function p using the ROC analysis, the following curve was obtained (Figure 15).Hazard ratios (HR) with 95% confidence intervals and p-values were calculated using the log-rank test.We defined the probability of hospital discharge in a given length of time while considering time in many small intervals.The day of discharge from the hospital was considered the target event.p > 0.05 shows statistically significant difference between medians of hospital discharge (standard therapy-11 days vs. Paxlovid therapy-9 days).The resultant regression model exhibits statistical significance (p < 0.001).A Nagelkerke R 2 value of 0.321 suggests a robust relationship between predictors and Paxlovid treatment.This model achieves an accuracy of 68.1%, accurately classifying the predictions.
When evaluating the dependence of the probability of odds on the value of logistic function p using the ROC analysis, the following curve was obtained (Figure 15).
The cut-off value of logistic function p which corresponds to the highest Youden's J statistic is 0.3.The specificity and sensitivity of the method were 67.3% and 69.6%, respectively.The area under the ROC curve comprised 0.79 with 95% CI: 0.68-0.91.The resulting model was statistically significant (p < 0.001).
The cut-off value of logistic function p which corresponds to the highest Youden's J statistic is 0.3.The specificity and sensitivity of the method were 67.3% and 69.6%, respectively.

Discussion
In this investigation, we aimed to assess the efficacy of Paxlovid (nirmatrelvir/ritonavir) in individuals with COVID-19, specifically considering the coexistence of MAFLD.No significant distinctions were observed between the MAFLD and non-MAFLD cohorts in terms of hospitalization duration, blood oxygen saturation, and oxygen supplementation requirements.Notably, Paxlovid treatment correlated with a reduction in hospitalization duration and elevated oxygen saturation levels at discharge, irrespective of the presence or absence of MAFLD.
Furthermore, no significant correlation was established between the severity of COVID-19 and the presence of MAFLD.However, a noteworthy association was identified between the severity of COVID-19, the occurrence of community-acquired pneumonia, diminished oxygen saturation levels, and the necessity for oxygen support.
It is imperative to acknowledge that these findings are applicable solely to the specified patient cohort, as the study was exclusively conducted among individuals of European origin (Ukrainians) aged 20 to 70 years.The observed results consider the presence of the aforementioned concurrent diseases and additional characteristics outlined in Table 1.
MAFLD manifests in approximately one in every four individuals globally, establishing it as one of the most prevalent causes of chronic liver disease (CLD) [25].Extant research has established a correlation between MAFLD and the manifestation of severe COVID-19 [26,27].Notably, individuals with MAFLD exhibit an elevated likelihood of experiencing abnormal liver function, thus heightening their susceptibility to the pro-

Discussion
In this investigation, we aimed to assess the efficacy of Paxlovid (nirmatrelvir/ritonavir) in individuals with COVID-19, specifically considering the coexistence of MAFLD.No significant distinctions were observed between the MAFLD and non-MAFLD cohorts in terms of hospitalization duration, blood oxygen saturation, and oxygen supplementation requirements.Notably, Paxlovid treatment correlated with a reduction in hospitalization duration and elevated oxygen saturation levels at discharge, irrespective of the presence or absence of MAFLD.
Furthermore, no significant correlation was established between the severity of COVID-19 and the presence of MAFLD.However, a noteworthy association was identified between the severity of COVID-19, the occurrence of community-acquired pneumonia, diminished oxygen saturation levels, and the necessity for oxygen support.
It is imperative to acknowledge that these findings are applicable solely to the specified patient cohort, as the study was exclusively conducted among individuals of European origin (Ukrainians) aged 20 to 70 years.The observed results consider the presence of the aforementioned concurrent diseases and additional characteristics outlined in Table 1.
MAFLD manifests in approximately one in every four individuals globally, establishing it as one of the most prevalent causes of chronic liver disease (CLD) [25].Extant research has established a correlation between MAFLD and the manifestation of severe COVID-19 [26,27].Notably, individuals with MAFLD exhibit an elevated likelihood of experiencing abnormal liver function, thus heightening their susceptibility to the progression of COVID-19 [28].The risk of developing severe COVID-19 is more than twofold higher among MAFLD patients compared to those without MAFLD, particularly for individuals below the age of 60 [29].Furthermore, there exists a recurrent association between patients with both metabolic syndrome (MetS) and abnormal liver function, leading to an increased incidence of Intensive Care Unit (ICU) admissions and a more severe trajectory of COVID-19 [30][31][32][33].
Hence, existing literature posits that individuals with MAFLD may be at augmented risk of experiencing severe COVID-19 [1,11,[34][35][36], necessitating intensive care and super-vision, requiring ICU-level supervision and care [1,34].Nevertheless, the body of evidence on this association is not devoid of conflicting data.Notably, a meta-analysis conducted by Li et al. in 2022 [37] failed to identify conclusive evidence supporting MAFLD as an independent risk factor for severe COVID-19.Instead, the study suggested that the apparent connection between MAFLD and COVID-19 severity may be explicable by the concurrent presence of obesity within this patient cohort.This assumption is explained by immune dysregulation observed in individuals with elevated BMI, thereby exacerbating COVID-19 symptoms.When considered collectively, effective weight control emerges as a potentially pivotal modifiable risk factor for averting the progression to severe COVID-19 [37].T2DM can also influence the immune system, potentially affecting the host response to COVID-19 [38].The interaction between T2DM, MAFLD, and the immune response to COVID-19 may lead to nuanced and interconnected effects that are difficult to disentangle [14].
The cellular entry of SARS-CoV-2 is facilitated through binding to angiotensin-converting enzyme-2 (ACE-2) receptors in human cells [39,40].This interaction is augmented by the fusion of the viral membrane with the host cell membrane, a process further facilitated by the priming of SARS-CoV-2 spike proteins through the activity of the host cell transmembrane protein, type II transmembrane serine protease (TMPRSS2) [39].Notably, individuals with pre-existing MAFLD exhibit an elevated expression of ACE-2 receptors, thereby heightening their susceptibility to the development of severe COVID-19 disease [41].Furthermore, observations by Shao et al. [42] revealed a noteworthy increase in the population of TM-PRSS2+ cells in cirrhotic livers, thereby exacerbating COVID-19 outcomes.This study posited that pre-existing MAFLD might enhance susceptibility to the SARS-CoV-2 virus, primarily due to an elevated count of TMPRSS2+ progenitor cells.
MAFLD instigates a persistent low-grade inflammatory state, primarily mediated through insulin resistance, and is closely associated with obesity and DM [43].These comorbidities, recognized contributors to adverse outcomes in COVID-19, are implicated in the chronic inflammatory milieu that detrimentally affects the immune system's responsiveness to infections, potentially exacerbating the severity of COVID-19 infection [28,29].The presence of pre-existing MAFLD further intensifies the acute inflammatory response induced by SARS-CoV-2 during active COVID-19 infection, leading to an escalation in the release of proinflammatory cytokines and reactive oxygen species [44,45].
In an investigation by Targher et al. [46], the relationship between imaging-defined MAFLD and the neutrophil-to-lymphocyte ratio (NLR) in MAFLD patients was scrutinized.The study revealed an elevated NLR and T lymphopenia in individuals with MAFLD compared to those without.Moreover, patients exhibiting increased NLRs experienced more adverse hospital outcomes, likely attributable to an augmented release of proinflammatory cytokines exacerbating the inflammatory/cytokine storm during active infection [46,47].
A retrospective analysis encompassing 202 individuals diagnosed with MAFLD revealed that these patients exhibited a prolonged period of viral shedding, lasting for 17.5 days in contrast to patients without MAFLD, who manifested a viral shedding duration of 12.1 days [28].The protracted viral shedding in MAFLD patients is attributed to a compromised immune response and systemic inflammation, impeding effective containment of the virus within the host body.Additionally, the obese microenvironment in metabolic syndrome/MAFLD is posited to suppress interferon production and elevate ACE-2 receptor expression in COVID-19 infection, thereby exacerbating viral RNA replication.Consequently, these factors collectively contribute to heightened viral infectivity and increased severity of the infection [48].These hypotheses underscore the synergistic nature of MAFLD and COVID-19 pathogenesis.
Numerous studies have presented evidence elucidating the reciprocal impact of liver diseases and COVID-19 on each other's disease trajectory.Existing hepatic steatosis and MAFLD have been identified as influencers of COVID-19 disease severity, Intensive Care Unit (ICU) admission rates, and the necessity for invasive mechanical ventilation.Conversely, COVID-19 contributes to the exacerbation of hepatic injury and the progression of disease severity in MAFLD and other liver disorders [14].However, it is essential to acknowledge that MAFLD frequently coexists with additional entities such as obesity and DM within the broader spectrum of metabolic syndrome.The intricate interplay between MAFLD and comorbidities like obesity and DM introduces challenges in establishing a direct causal link between MAFLD and COVID-19 outcomes independent of these associated comorbidities.
An avenue to comprehend the intricate interplay between MAFLD and COVID-19 involves the exploration of key genes and pathways implicated in these conditions.This approach holds promise for discerning potential drug targets and biomarkers.In a study by Karami et al. [49], a methodological framework encompassing weighted gene co-expression network analysis and LIME, an explainable artificial intelligence algorithm, was applied.This methodology successfully identified 17 novel FDA-approved candidate drugs.These drugs have the potential to be utilized in the treatment of COVID-19 patients through the regulation of four hub genes within the co-expression network.The identification of co-regulated gene networks and hub genes through such an approach has the capacity to unveil critical biological pathways.
Numerous genetic polymorphisms, such as PNPLA3 (rs738409), GCKR (rs780094), TM6SF2 (rs58542926), and LYPLAL1 (rs12137855), have undergone scrutiny concerning their association with MAFLD susceptibility and progression.Certain studies propose a plausible correlation between these MAFLD-associated polymorphisms and the severity of COVID-19 [35].It is imperative to explore the potential synergistic effects of these genetic polymorphisms, thereby contributing to a comprehensive understanding of the intricate interplay between MAFLD susceptibility and the outcomes of COVID-19.
Individuals deemed at risk for developing severe and critical illness subsequent to COVID-19 infection are advised to undergo nirmatrelvir/ritonavir therapy [50].Presently, the "Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 9)" [50] advocates for the administration of this therapeutic regimen to COVID-19 patients within the initial 5 days following the onset of symptoms, with the aim of forestalling the progression to severe illness.Furthermore, the U.S. Food and Drug Administration (FDA) has granted approval for the use of this drug in adolescent patients aged 12 years and above, with a body weight of ≥40 kg [51].
Nirmatrelvir, functioning as a peptidomimetic inhibitor, specifically targets the main protease (Mpro) of the coronavirus, thereby impeding viral replication.Its primary metabolic pathway involves CYP3A4.Concurrently, ritonavir, an inhibitor of HIV-1 protease, enhances the blood concentration of nirmatrelvir by inhibiting the enzymatic activity of CYP3A4, thereby synergistically augmenting its effectiveness.The elimination pathways for these compounds differ, with nirmatrelvir primarily undergoing renal excretion and ritonavir undergoing hepatic metabolism [52].
A multitude of studies, encompassing vaccinated participants, consistently reported the efficacy of nirmatrelvir/ritonavir in reducing hospitalization and mortality rates, even in the context of prevalent omicron and BA4/5 variants.However, the observed degree of effectiveness exhibited variability across the spectrum of studies [53][54][55][56][57][58][59].Several of these studies were conducted during the periods characterized by the Delta and Omicron variants, potentially leading to varying effectiveness compared to earlier stages.Nirmatrelvir/ritonavir exhibited favorable tolerance and efficacy in patients with the Omicron variant of COVID-19 [60].
In the EPIC-HR trial, among non-hospitalized individuals with mild-to-moderate COVID-19 who were unvaccinated and at risk of progressing to severe disease, the early initiation of nirmatrelvir plus ritonavir within 5 days of symptom onset resulted in a notable relative reduction of 88% in the composite outcome of hospitalization or death [18].Conversely, the updated analysis of the EPIC-SR trial, which involved unvaccinated adults at standard risk of COVID-19 or fully vaccinated individuals with at least one risk factor, indicated a non-significant reduction of 51% in hospitalization or death with the use of nirmatrelvir plus ritonavir in non-hospitalized patients [61].Nirmatrelvir-ritonavir treatment demonstrated an association with fewer emergency department visits in the 28 days following administration compared to matched, untreated patients.This finding aligns with a single-arm study by Malden and colleagues, which reported emergency department visits or hospitalizations occurring with less than 1% frequency in the 5-15 days after nirmatrelvir-ritonavir treatment [62].Aggarwal NR, et al. [56] outed potential benefits of nirmatrelvir-ritonavir in both older and younger patients, as did Zhou X, et al. [63] and Shah M, et al. [64].Notably, a study by Arbel and colleagues found a reduction in hospitalization only in COVID-19-positive outpatients aged 65 years or older after nirmatrelvir-ritonavir treatment, with no apparent benefit observed in those younger than 65 years [53].
The findings from the meta-analysis conducted by Amani B. et al. [65] underscored a significant association between Paxlovid treatment and a markedly lower mortality rate in COVID-19 patients compared to control groups.Notably, Paxlovid-treated individuals exhibited a significantly lower rate of hospitalization or death in comparison to those not receiving Paxlovid.These results align with the meta-analysis by Zheng et al. [66], who similarly demonstrated a reduction in the death rate among COVID-19 patients treated with Paxlovid, emphasizing a significant clinical benefit in terms of reduced hospitalization rates compared to those who did not receive Paxlovid.Furthermore, a meta-analysis encompassing three new oral antivirals-molnupiravir, fluvoxamine, and Paxlovid-revealed that Paxlovid treatment was linked to a significantly lower mortality rate in COVID-19 patients compared to placebo, highlighting the efficacy of Paxlovid, molnupiravir, and fluvoxamine in mitigating the hospitalization rate due to COVID-19 [67].
Results from a recently published randomized controlled trial (RCT) involving nonhospitalized adults at high risk of progression to COVID-19 [18] demonstrated a lower frequency of Grade 3 or 4 adverse events, serious adverse events, and adverse events leading to discontinuation in the Paxlovid group as opposed to the placebo group.Moreover, data from a large cohort of 183,041 COVID-19 patients indicated no significant difference between the Paxlovid and no antiviral treatments concerning a higher risk of abnormal liver enzymes or drug-induced liver injury (DILI) [68].These findings are consistent with a meta-analysis examining adverse events associated with the oral antiviral molnupiravir, which showed no significant difference in the incidence of adverse events in COVID-19 patients compared to the control group [69].
The studies in the discussion section were heterogeneous in terms of study designs, patient populations, treatment protocols, the presence of randomization, patients vaccinated with different COVID-19 vaccines, and the absence of vaccination.There were variations in the severity of the disease in outpatient and inpatient treatment settings.However, nirmatrelvir-ritonavir (Paxlovid) demonstrated high efficacy across all cases.
In our study, we relied on the use of targeted antiviral therapy for COVID-19, as it did not affect the course of MAFLD.We tried to find out the effectiveness of nirmatrelvirritonavir (Paxlovid) treatment in such patients.
Nevertheless, we studied a well-defined cohort of patients and reported the first data examining the effectiveness of nirmatrelvir-ritonavir (Paxlovid) treatment in patients with MAFLD and COVID-19.Longitudinal studies are needed to find out the significance of targeted antiviral therapy for COVID-19 in patients with components of metabolic syndrome and MAFLD.

Figure 1 .
Figure 1.(a) ROC curve characterizing the dependence of the probability of the need for oxygen supply on the value of logistic function P.This ROC curve assesses the quality of logistic regression for predicting the primary outcome.It was created using the prediction results of the regression model and the category we are trying to predict.(b) Cut-off plot with the best cut-off point to maximize specificity and sensitivity indicators.

Figure 1 .
Figure 1.(a) ROC curve characterizing the dependence of the probability of the need for oxygen supply on the value of logistic function P.This ROC curve assesses the quality of logistic regression for predicting the primary outcome.It was created using the prediction results of the regression model and the category we are trying to predict.(b) Cut-off plot with the best cut-off point to maximize specificity and sensitivity indicators.

Figure 2 .
Figure 2. Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.Paxlovid treatment significantly reduced the length of hospital stay in both COVID-19 with MAFLD (10 days, IQR 8-11 days vs. 11.5 days, IQR 10-14.25 days, p = 0.025) and COVID-19 without MAFLD (8 days, IQR 7-9 days vs. 11 days, IQR 8-14 days, p = 0.018) cohort (Figure3).The presence of MALFD did not show any significant effect on the duration of hospitalization in both Paxlovid and standard treatment cohorts.

Figure 2 .
Figure 2. Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.Paxlovid treatment significantly reduced the length of hospital stay in both COVID-19 with MAFLD (10 days, IQR 8-11 days vs. 11.5 days, IQR 10-14.25 days, p = 0.025) and COVID-19 without MAFLD (8 days, IQR 7-9 days vs. 11 days, IQR 8-14 days, p = 0.018) cohort (Figure3).The presence of MALFD did not show any significant effect on the duration of hospitalization in both Paxlovid and standard treatment cohorts.

Figure 4 .
Figure 4. Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and pvalues were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.

Figure 4 .
Figure 4. Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.
without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.

Figure 6 .
Figure 6.Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.

Figure 6 .
Figure 6.Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.

Figure 8 .
Figure 8.Comparison of the medians of four groups: patients treated with standard therapy vs. those treated with Paxlovid (disregarding the presence of MALFD) on the left; patients with and without MAFLD (disregarding the treatment) on the right.Data are presented as medians and p-values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range.

Figure 4 .Figure 10 .
Figure 4. Comparison of the medians of four groups: patients treated with standard those treated with Paxlovid (disregarding the presence of MALFD) on the left; patien without MAFLD (disregarding the treatment) on the right.Data are presented as med values were calculated using the Mann-Whitney test.IQR-25-75% interquartile range

Figure 10 .
Figure 10.The difference in the medians of the clinical and laboratory findings in patients with Paxlovid and standard therapy at discharge compared with admission.Data are presented as medians with IQR, and p-values were calculated using Wilcoxon matched-pairs test.IQR-5-75% interquartile range.

Figure 11 .
Figure 11.The difference in the medians of the clinical and laboratory findings in patients with Paxlovid and standard therapy at discharge compared with admission.Data are presented as medians with IQR, and p-values were calculated using Wilcoxon matched-pairs test.IQR-25-75% interquartile range.

Figure 11 .
Figure 11.The difference in the medians of the clinical and laboratory findings in patients with Paxlovid and standard therapy at discharge compared with admission.Data are presented as medians with IQR, and p-values were calculated using Wilcoxon matched-pairs test.IQR-25-75% interquartile range.

Figure 12 .
Figure 12.The difference in the medians of the clinical and laboratory findings in patients with Paxlovid and standard therapy at discharge compared with admission.Data are presented as medians with IQR, and p-values were calculated using Wilcoxon matched-pairs test.IQR-25-75% interquartile range.

Figure 13 .
Figure 13.Correlation correlogram.Spearman's correlation was used with two continuous variables, point-biserial correlation between binary and continuous data, the Chi-square test between two binary data.The color at the intersection of those variables represents the strength of the correlation between two variables.Colors range from crimson (strong negative correlation; r = −1.0) to cyan blue (strong positive correlation; r = 1.0).Results were not represented if p > 0.05.

Figure 13 .
Figure 13.Correlation correlogram.Spearman's correlation was used with two continuous variables, point-biserial correlation between binary and continuous data, the Chi-square test between two binary data.The color at the intersection of those variables represents the strength of the correlation between two variables.Colors range from crimson (strong negative correlation; r = −1.0) to cyan blue (strong positive correlation; r = 1.0).Results were not represented if p > 0.05.

Figure 14 .
Figure 14.Association of time to recovery with Paxlovid prescription using Kaplan-Meier curves in patients with COVID-19.Hazard ratios (HR) with 95% confidence intervals and p-values were calculated using the log-rank test.We defined the probability of hospital discharge in a given length of time while considering time in many small intervals.The day of discharge from the hospital was considered the target event.p > 0.05 shows statistically significant difference between medians of hospital discharge (standard therapy-11 days vs. Paxlovid therapy-9 days).

Figure 14 .
Figure 14.Association of time to recovery with Paxlovid prescription using Kaplan-Meier curves in patients with COVID-19.Hazard ratios (HR) with 95% confidence intervals and p-values were calculated using the log-rank test.We defined the probability of hospital discharge in a given length of time while considering time in many small intervals.The day of discharge from the hospital was considered the target event.p > 0.05 shows statistically significant difference between medians of hospital discharge (standard therapy-11 days vs. Paxlovid therapy-9 days).

Figure 15 .
Figure 15.(a) ROC curve characterizing the dependence of the probability of the need for oxygen supply on value of logistic function P.This ROC curve assesses the quality of logistic regression for predicting the primary outcome.It was created using the prediction results of the regression model and the category we are trying to predict; (b) cut-off plot with the best cut-off point to maximize specificity and sensitivity indicators.

Figure 15 .
Figure 15.(a) ROC curve characterizing the dependence of the probability of the need for oxygen supply on value of logistic function P.This ROC curve assesses the quality of logistic regression for predicting the primary outcome.It was created using the prediction results of the regression model and the category we are trying to predict; (b) cut-off plot with the best cut-off point to maximize specificity and sensitivity indicators.
a Fisher exact, Chi-square or Mann-Whitney U test, as appropriate; b data are presented as medians (interquartile range).Abbreviations: IQR-interquartile range; COPD-chronic obstructive pulmonary disease.Laboratory findings at admission are shown in

Table 2 .
Laboratory finding on admission/discharge.

Table 4 .
Estimating parameters in logistic regression for the need for oxygen supply.

Table 5 .
Difference in laboratory findings in patients treated with Paxlovid/standard therapy on discharge.
Wilcoxon signed-rank test was used for comparing two related groups.Mann-Whitney U test was used to compare the two independent groups.

Table 6 .
Estimating factors in logistic regression factors to predict Paxlovid therapy.

Table 6 .
Estimating factors in logistic regression factors to predict Paxlovid therapy.